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Title:

Methods and Measures for Semantic Network Analysis

Version:

1.4.5

Date:

2025-11-03

Maintainer:

Alexander P. Christensen <alexpaulchristensen@gmail.com>

Description:

Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf), and a function to compute global network measures. Significance tests and plotting features are also implemented.

Depends:

R (≥ 3.6.0)

License:

GPL (≥ 3.0)

Encoding:

UTF-8

LazyData:

true

Imports:

pbapply, dplyr, plyr, magrittr, ggplot2, igraph, qgraph,scales, car, broom, effects, methods, philentropy

URL:

https://github.com/AlexChristensen/SemNeT

BugReports:

https://github.com/AlexChristensen/SemNeT/issues

NeedsCompilation:

Suggests:

shiny, shinyjs, shinyalert, shinyMatrix, shinyBS, animation,R.matlab, foreign, readxl, data.table, NetworkToolbox,SemNetCleaner, SemNetDictionaries

RoxygenNote:

7.3.3

Packaged:

2025-11-03 11:41:00 UTC; alextops

Author:

Alexander P. Christensen

[aut, cre], Yoed N. Kenett

[aut, ctb]

Repository:

CRAN

Date/Publication:

2025-11-03 13:10:02 UTC

SemNeT–package

Description

Implements several functions for the analysis of semanticnetworks including partial node bootstraping (Kenett, Anaki, & Faust, 2014),random walk simulation (Kenett & Austerweil, 2016), and a function to computeglobal network measures. Significance tests and plotting features are alsoimplemented.

Author(s)

Alexander P. Christensen <alexpaulchristensen@gmail.com> & Yoed N. Kenett <yoedkenett@gmail.com>

References

Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018).Remotely close associations: Openness to experience and semantic memory structure.European Journal of Personality,32, 480-492.

Kenett, Y. N., Anaki, D., & Faust, M. (2014).Investigating the structure of semantic networks in low and high creative persons.Frontiers in Human Neuroscience,8, 407.

Kenett, Y. N., & Austerweil, J. L. (2016).Examining search processes in low and high creative individuals with random walks.InPaper presented at the proceedings of the 38th annual meeting of the cognitive science society. Austin, TX.

Average Shortest Path Length

Description

Computes the global average shortest path length of the network

Usage

ASPL(A, weighted = FALSE)

Arguments

A

An adjacency matrix of network data

weighted

Is the network weighted?Defaults toFALSE.Set toTRUE for weighted measures

Value

Returns the ASPL of the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations.NeuroImage,52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checksA <- TMFG(similarity(sim.fluency(100), method = "cor"))# Unweightedaspl <- ASPL(A)

Clustering Coefficient

Description

Computes global clustering coefficient CC

Usage

CC(A, weighted = FALSE)

Arguments

A

An adjacency matrix of network data

weighted

Is the network weighted?Defaults toFALSE.Set toTRUE for weighted measures of CC and CCi

Value

Returns the network's CC

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations.NeuroImage,52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checksA <- TMFG(similarity(sim.fluency(100), method = "cor"))# Unweightedcc <- CC(A)

Community Network Estimation

Description

Estimates a semantic network using the Community Networkmethod described in Goni et al. (2011)

Usage

CN(data, window = 2, alpha = 0.05, enrich = FALSE)

Arguments

data

Matrix or data frame.A preprocessed verbal fluency matrix whererows are participants and columns are verbal fluencyresponses

window

Numeric.Size of window to look for co-occurences in.Defaults to2

alpha

Numeric.Significance value.Defaults to.05

enrich

Boolean.Should the network be enriched by connectingall nodes in their respective modules?Defaults toFALSE

Value

Returns a undirected semantic network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Goni, J., Arrondo, G., Sepulcre, J., Martincorena, I., de Mendizabal, N. V., Corominas-Murtra, B., ... & Villoslada, P. (2011).The semantic organization of the animal category: Evidence from semantic verbal fluency and network theory.Cognitive Processing,12, 183-196.

Examples

# Get datadata <- open.clean# Organize group data## Get group datagroup <- open.group## Low and high openness to experience groupslow <- data[which(group == "Low"),]high <- data[which(group == "High"),]## Not run: # Compute networkslow.net <- CN(low)high.net <- CN(high)## End(Not run)

Naive Random Walk Network Estimation

Description

Estimates a semantic network using the Naive Random Walkmethod described in Lerner, Ogrocki, and Thomas (2009)

Usage

NRW(data, type = c("num", "prop"), threshold = 0)

Arguments

data

Matrix or data frame.A preprocessed verbal fluency matrix whererows are participants and columns are verbal fluencyresponses

type

Character.Type ofthreshold to apply.

"num" — Minimum number of co-occurrences
"prop" — Minimum proportion of co-occurrences

Defaults to"num"

threshold

Numeric.Value of the minimum number or proportion of co-occurrences.Defaults to0 for both"num" and"prop"

Value

Returns a undirected semantic network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Lerner, A. J., Ogrocki, P. K., & Thomas, P. J. (2009).Network graph analysis of category fluency testing.Cognitive and Behavioral Neurology,22, 45-52.

Examples

# Get datadata <- open.clean# Organize group data## Get group datagroup <- open.group## Low and high openness to experience groupslow <- data[which(group == "Low"),]high <- data[which(group == "High"),]# Compute networkslow.net <- NRW(low)high.net <- NRW(high)

Pathfinder Network

Description

Estimates a pathfinder network using the MST-PathfinderNetwork method from Quirin et al. (2008; see also Schvaneveldt, 1990)

Usage

PF(data)

Arguments

data

Matrix or data frame.A binary response matrix

Value

An adjacency matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Quirin, A., Cordon, O., Guerrero-Bote, V. P., Vargas-Quesada, B., & Moya-Aneon, F. (2008)A quick MST-based algorithm to obtain Pathfinder networks (Inf, n-1).Journal of the American Society for Information Science and Technology,59, 1912-1924.

Schvaneveldt, R. W. (1990).Pathfinder associative networks: Studies in knowledge organization.Norwood, NJ: Ablex Publishing.

Examples

# Obtain datadata <- open.binary# Estimate networkpf.net <- PF(data)

Modularity

Description

Computes a global modularity measure (Q) using the Louvain community detection algorithm

Usage

Q(A)

Arguments

A

An adjacency matrix of network data

Value

Returns Q or a measure of how well the communities in the network are compartmentalized

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008).Fast unfolding of communities in large networks.Journal of Statistical Mechanics: Theory and Experiment,2008, P10008.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations.NeuroImage,52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checksA <- TMFG(similarity(sim.fluency(100), method = "cor"))modularity <- Q(A)

Shiny App for`SemNeT`

Description

An interactive Shiny application for runningSemNeT analysis.

Usage

SemNeTShiny()

Value

A list calledresultShiny containing:

data

The data imported intoSemNeTShiny

group

The grouping variable imported intoSemNeTShiny

network

The networks generated duringSemNeTShiny session.The networks are labelled using the provided grouping variable

measures

Network measures ASPL (Average Shortest Path Lengths),CC (Clustering Coefficient), and Q (Modularity) for the networks generatedduringSemNeTShiny

(seesemnetmeas)

comparePlot

A visualization of the networks generated duringSemNeTShiny

(seecompare_nets)

randomTest

Statistical results from the Random Network Test inSemNeTShiny(seerandnet.test)

bootstrap

Results from the Bootstrap Network Analysis inSemNeTShiny(seebootSemNeT)

bootstrapTest

Statistical results from the Bootstrap Network Analysis(seetest.bootSemNeT)

bootstrapPlot

Plots of the statistical results from the Bootstrap Network Analysis(seeplot.bootSemNeT)

randomWalk

Results from the Random Walk Analysis(seerandwalk)

bootstrapPlot

Results from the Spreading Activation Analysis(seespreadr)

Examples

if(interactive()){SemNeTShiny()}

Triangulated Maximally Filtered Graph

Description

Applies the Triangulated Maximally Filtered Graph (TMFG) filtering method(Please see and cite Massara et al., 2016). The TMFG method uses a structuralconstraint that limits the number of zero-order correlations included in the network(3n - 6; wheren is the number of variables). The TMFG algorithm begins byidentifying four variables which have the largest sum of correlations to all othervariables. Then, it iteratively adds each variable with the largest sum of threecorrelations to nodes already in the network until all variables have been added tothe network. This structure can be associated with the inverse correlation matrix(i.e., precision matrix) to be turned into a GGM (i.e., partial correlation network)by usingLoGo. See Details for more information on thisnetwork estimation method.

Usage

TMFG(data, depend = FALSE)

Arguments

data

Matrix or data frame.Must be a square matrix

depend

Boolean.Is network a dependency (or directed) network?Defaults toFALSE.Set toTRUE to generate a TMFG-filtered dependency network(output obtained from thedepend function)

Details

The TMFG method applies a structural constraint on the network,which restrains the network to retain a certain number of edges (3n-6, wherenis the number of nodes; Massara et al., 2016). The network is also composed of 3- and 4-nodecliques (i.e., sets of connected nodes; a triangle and tetrahedron, respectively). TheTMFG method constructs a network using zero-order correlations and the resulting networkcan be associated with the inverse covariance matrix(yielding a GGM; Barfuss, Massara, Di Matteo, & Aste, 2016).Notably, the TMFG can use any association measure and thus does not assume the data is multivariate normal.

Construction begins by forming a tetrahedron of the four nodes that havethe highest sum of correlations that are greater than the average correlation in thecorrelation matrix. Next, the algorithm iteratively identifies the node that maximizesits sum of correlations to a connected set of three nodes (triangles) already includedin the network and then adds that node to the network. The process is completed onceevery node is connected in the network. In this process, the network automaticallygenerates what's called a planar network. A planar network is a network that could bedrawn on a sphere with no edges crossing (often, however, the networks are depictedwith edges crossing; Tumminello, Aste, Di Matteo, & Mantegna, 2005).

Value

Returns an TMFG filtered adjacency matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018).Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches.Behavior Research Methods,50, 2531-2550.

Massara, G. P., Di Matteo, T., & Aste, T. (2016).Network filtering for big data: Triangulated maximally filtered graph.Journal of Complex Networks,5, 161-178.

Examples

# Pearson's correlation only for CRAN checksA <- TMFG(similarity(sim.fluency(100), method = "cor"))

Frequency of Animal Responses

Description

Frequency of animal responses from Christensen & Kenett (2019). These frequenciesare used to generate data in thesim.fluency function.

Usage

data(animals.freq)

Format

animals.freq (vector, length = 367)

Examples

data("animals.freq")

Bootstrapped Semantic Network Analysis

Description

Bootstrap techniques to generatesemantic networks and compute global network characteristics

Usage

bootSemNeT(  ...,  method = c("CN", "NRW", "PF", "TMFG"),  methodArgs = list(),  type = c("case", "node"),  prop = 0.5,  sim,  weighted = FALSE,  iter = 1000,  cores)

Arguments

...

Matrices or data frames.Cleaned response matrices (e.g.,responses$clean fromtextcleaner) or binary response matrices(e.g.,binary output fromtextcleaner)

method

Character.Network estimation method to use.Current options include:

TMFG — Triangulated Maximally Filtered Graph
CN — Community Network
NRW — Naive Random Walk
PF — Pathfinder

methodArgs

List.A list of additional arguments for the network estimation function.See links in argumentmethod for additional arguments (see also Examples)

type

Character.Type of bootstrap to perform

node — Generates partial networks based on dropping a certainproportion of nodes (see argumentprop)
case —Samples with replacement the same number of participantsas in the original dataset

prop

Numeric.Only fortype = "node".Proportion of nodes to remain in the network.Defaults to.50

sim

Character.Similarity measure to use.Defaults to"cosine".Seesimilarity for other options

weighted

Boolean.Should weighted ASPL and CC be used?Defaults toFALSE.Set toTRUE for weighted ASPL and CC

iter

Numeric.Number of iterations in bootstrap.Defaults to1000

cores

Numeric.Number of computer processing cores to use for bootstrapping samples.Defaults ton / 2 total number of cores.Set to any number between 1 and maximum amount of cores on your computer(seeparellel::detectCores())

Value

Returns a list containing:

dataMeas

A matrix for the network input in thedataargument, where columns are the semantic network measuresfromsemnetmeas and rows are their values from eachbootstrapped sample (results in a matrix with the dimensionsiter by 3)

dataSumm

Summary statistics across the bootrapped samples for thenetwork input in thedata argument

prop

Outputs the proportion used from theprop argument

iter

Outputs the number of bootstrapped samplesused from theiter argument

If apaired network is input, then also returns:

pairedMeas

A matrix for the network input in thepairedargument, where columns are the semantic network measuresfromsemnetmeas and rows are their values from eachbootstrapped sample (results in a matrix with the dimensionsiter by 3)

pairedSumm

Summary statistics across the bootrapped samples for thenetwork input in thepaired argument

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Datasetone <- sim.fluency(20)# Run bootstrap node-drop (partial) networksone.result <- bootSemNeT(one, prop = .50, iter = 100,sim = "cosine", cores = 2, method = "TMFG", type = "node")# Run bootstrap case-drop networks## Includes additional equating argument: minCaseone.result <- bootSemNeT(one, iter = 100, sim = "cosine",cores = 2, method = "TMFG", type = "case", methodArgs = list(minCase = 2))# Bootstrap case-wise networks## Get openness datalow <- open.clean[which(open.group == "Low"),]high <- open.clean[which(open.group == "High"),]## Run### Inlcudes additional NRW argument: thresholdopen <- bootSemNeT(low, high, iter = 100, cores = 2, method = "NRW", type = "case",methodArgs = list(type = "num", threshold = 3))

Plots Networks for Comparison

Description

Usesqgraph to plot networks.Accepts any number of networks and will organize the plotsin the number of side-by-side columns using the heuristic of taking the square root of the number ofinput and rounding down to the nearest integer (i.e.,floor(sqrt(length(input)))).

Examples

3 networks: 1 x 3
6 networks: 2 x 3
9 networks: 3 x 3

Usage

compare_nets(  ...,  title,  config,  placement = c("match", "default"),  weighted = FALSE,  qgraph.args = list())

Arguments

...

Matrices or data frames of network adjacency matrices

title

List.Characters denoting titles of plots

config

Character.Defaults to"spring"Seeqgraph for more options

placement

Character.How should nodes be placed when comparing groups?Defaults to"default"

"match" — places nodes in the same position for all networks
"default" — places nodes in the defaultconfig positions

weighted

Boolean.Should networks be plotted with weights?Defaults toFALSE.Set toTRUE to plot networks with weightscorresponding to association strength. Often, unweightednetworks are more aesthetically representational of thenetworks

qgraph.args

List.An argument list to be passed ontoqgraph.Seeqgraph for possible arguments

Value

Plots networks usingqgraph

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012).qgraph: Network visualizations of relationships in psychometric data.Journal of Statistical Software,48, 1-18.

Jones, P. J. (2019).networktools: Tools for Identifying Important Nodes in Networks.R package version 1.2.1.

Jones, P. J., Mair, P., & McNally, R. (2018).Visualizing psychological networks: A tutorial in R.Frontiers in Psychology,9, 1742.

Examples

# Simulate Datasetsone <- sim.fluency(10)two <- sim.fluency(10)# Compute similarity matrixcos1 <- similarity(one, method = "cosine")cos2 <- similarity(two, method = "cosine")# Compute networksnet1 <- TMFG(cos1)net2 <- TMFG(cos2)# Compare networkscompare_nets(net1, net2, title = list("One", "Two"), config = "spring")# Change edge colorscompare_nets(net1, net2, title = list("One", "Two"),config = "spring", qgraph.args = list(edge.color = "blue"))

Convert Adjacency Matrix to Cytoscape Format

Description

Converts an adjacency matrix to Cytoscape's sparse matrix format

Usage

convert2cytoscape(A)

Arguments

A

Matrix or data frame.A cleaned, finalized response matrix ready to be visualized

Value

A sparse matrix formatted for Cytoscape

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., ... & Ideker, T. (2003).Cytoscape: A software environment for integrated models of biomolecular interaction networks.Genome Research,13, 2498-2504.

Examples

# Simulate Datasetsone <- sim.fluency(10)two <- sim.fluency(10)# Compute similarity matrixcos1 <- similarity(one, method = "cosine")cos2 <- similarity(two, method = "cosine")# Compute networksnet1 <- TMFG(cos1)net2 <- TMFG(cos2)# Convert to Cytoscape formatcyto1 <- convert2cytoscape(net1)cyto2 <- convert2cytoscape(net2)# Write to .csvwrite.csv(cyto1, file.path(tempdir(), "cyto1.csv"), row.names = FALSE)write.csv(cyto2, file.path(tempdir(), "cyto2.csv"), row.names = FALSE)

Convert Network(s) to igraph's Format

Description

Converts single or multiple networks intoigraph's format for network analysis

Usage

convert2igraph(A, neural = FALSE)

Arguments

A

Adjacency matrix (network matrix) or brain connectivity array(fromconvertConnBrainMat)

neural

Defunct.

Value

Returns a network matrix inigraph's format

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checksA <- TMFG(similarity(sim.fluency(50), method = "cor"))igraphNetwork <- convert2igraph(A)

Equate Groups

Description

A function to "equate" multiple response matrices to one another.N number of groups are matched based on their responses sothat every group has the same responses in their data

Usage

equate(...)

Arguments

...

Matrices, data frames or a list of matrices and data frames.Binary response matrices to be equated

Value

This function returns a list containing theequated binary response matrices in the order they were input.The response matrices are labeled as the object name they wereentered with

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Obtain binary databin <- open.binary# Finalize mat1mat1 <- finalize(bin[c(1:5),])# Finalize mat2mat2 <- finalize(bin[c(6:10),])# Equate mat1 and mat1eq <- equate(mat1, mat2)# Obtain respective equated response matriceseq.mat1 <- eq$mat1 # list objects are named with the nameseq.mat2 <- eq$mat2 # they were entered with

Finalize Response Matrix

Description

Finalizes the response matrix by keepingresponses that are given by a certain number of people

Usage

finalize(rmat, minCase = 2)

Arguments

rmat

Binary matrix.Atextcleaner filtered response matrix

minCase

Numeric.Minimum number of cases to produce a response

Value

A binary response matrix with responsesgiven by at leastminCase people

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Obtain binary databin <- open.binary# Finalize mat1mat1 <- finalize(bin)

High Openness to Experience Network

Description

High openness to experience network from Christensen & Kenett (2019)

Usage

data(net.high)

Format

net.high (matrix, 160 x 160)

References

Christensen, A. P., & Kenett, Y. N. (2019)Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks.PsyArXiv.

Examples

data("net.high")

Low Openness to Experience Network

Description

Low openness to experience network from Christensen & Kenett (2019)

Usage

data(net.low)

Format

net.low (matrix, 160 x 160)

References

Christensen, A. P., & Kenett, Y. N. (2019)Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks.PsyArXiv.

Examples

data("net.low")

Simulated Result for Dataset One

Description

A result ofbootSemNeT from a simulated dataset

Usage

data(one.result)

Format

one.result (list, length = 4)

Examples

data("one.result")

Binary response Matrices (Openness and Verbal Fluency)

Description

Binary response matrices for the Animals verbal fluency data (n = 516)from Christensen et al. (2018).

Usage

data(open.binary)

Format

open.binary (matrix, 516 x 367)

References

Examples

data("open.binary")

Cleaned response Matrices (Openness and Verbal Fluency)

Description

Cleaned response matrices for the Animals verbal fluency data (n = 516)from Christensen et al. (2018).

Usage

data(open.clean)

Format

open.clean (matrix, 516 x 35)

References

Examples

data("open.clean")

Groups for Openness and Verbal Fluency

Description

Groups for the Animals verbal fluency data (n = 516)from Christensen et al. (2018; see alsoopen.clean).

Usage

data(open.group)

Format

open.group (vector, length = 516)

References

Examples

data("open.group")

Animate Networks for Spreading Activation from Shiny

Description

Usesqgraph andani.recordto animate networks. Accepts only one network animation at a time

Usage

## S3 method for class 'animateShiny'plot(x, ...)

Arguments

x

Shiny resultresultShiny$spreadingActivationPlot

...

Additional arguments forani.record

Value

Plots animated networks usingqgraph andani.record

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012).qgraph: Network visualizations of relationships in psychometric data.Journal of Statistical Software,48, 1-18.Retrieved from: http://www.jstatsoft.org/v48/i04/

Siew, C. S. Q. (2019).spreadr: An R package to simulate spreading activation in a network.Behavior Research Methods,51, 910-929.https://doi.org/10.3758/s13428-018-1186-5

Examples

if(interactive()){SemNeTShiny()}## Not run:   plot(resultShiny$spreadingActivationPlot[[1]])## End(Not run)

Plot forbootSemNeT

Description

Plots output frombootSemNeT

Usage

## S3 method for class 'bootSemNeT'plot(..., groups = NULL, measures = c("ASPL", "CC", "Q"))

Arguments

...

Object(s) frombootSemNeT

groups

Character.Labels for groups in the order they were enteredinbootSemNeT

measures

Character.Measures to be plotted

Value

Returns plots for the specified measures

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Datasetone <- sim.fluency(20)# Run partial bootstrap networksone.result <- bootSemNeT(one, prop = .50, iter = 1000,sim = "cosine", cores = 2, type = "node", method = "TMFG")# Plotplot(one.result, groups = c("One"))

Plots Networks for Comparison from Shiny

Description

Usesqgraphto plot networks. Accepts any number of networks and will organize the plotsin the number of side-by-side columns using the heuristic of taking the square root of the number ofinput and rounding down to the nearest integer (i.e.,floor(sqrt(length(input)))).Performs the same operations ascompare_nets

Examples

3 networks: 1 x 3
6 networks: 2 x 3
9 networks: 3 x 3

Usage

## S3 method for class 'compareShiny'plot(x, ...)

Arguments

x

Shiny resultresultShiny$comparePlot

...

Additional arguments

Value

Plots networks usingqgraph

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012).qgraph: Network visualizations of relationships in psychometric data.Journal of Statistical Software,48, 1-18.

Jones, P. J. (2019).networktools: Tools for Identifying Important Nodes in Networks.R package version 1.2.1.

Jones, P. J., Mair, P., & McNally, R. (2018).Visualizing psychological networks: A tutorial in R.Frontiers in Psychology,9, 1742.

Examples

# Simulate Datasetsone <- sim.fluency(10)two <- sim.fluency(10)# Compute similarity matrixcos1 <- similarity(one, method = "cosine")cos2 <- similarity(two, method = "cosine")# Compute networksnet1 <- TMFG(cos1)net2 <- TMFG(cos2)# Compare networkscompare_nets(net1, net2, title = list("One", "Two"), config = "spring")# Change edge colorscompare_nets(net1, net2, title = list("One", "Two"),config = "spring", qgraph.args = list(edge.color = "blue"))

Test Against Random Networks

Description

Performs significance tests for global measuresof semantic networks against the global measures of equivalentsize (and density) random networks

Usage

randnet.test(..., iter, cores)

Arguments

...

Matrices or data frames.Semantic networks to be compared against random networks

iter

Numeric.Number of iterations in bootstrap.Defaults to1000

cores

Number of computer processing cores to use for bootstrapping samples.Defaults ton - 1 total number of cores.Set to any number between 1 and maximum amount of cores on your computer

Value

Returns a matrix containing p-valuesfor the network measures of the input networks againstthe distribution of equivalent random networks. The lasttwo columns contain the mean ("M.rand") andstandard deviation ("SD.rand") of the network measuresfor the random network distribution

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Viger, F., & Latapy, M. (2016).Efficient and simple generation of random simple connected graphs with prescribed degree sequence.Journal of Complex Networks,4, 15-37.

Examples

# Get openness dataone <- open.clean[which(open.group == "Low"),]two <- open.clean[which(open.group == "High"),]# Compute networksnet.one <- CN(one)net.two <- CN(two)# Perform random networks testrandnet.test(net.one, net.two, iter = 100, cores = 2)

Random Walk Simulation

Description

Simulates random walks over two networks to examine the characteristicsof spontaneous spreading activation (see Kenett & Austerweil, 2016)

Usage

randwalk(A, B, reps = 20, steps = 10, iter = 10000, cores)

Arguments

A

Matrix or data frame.Adjacency matrix of a semantic network

B

Matrix or data frame.A comparison adjacency matrix of a semantic network

reps

Numeric.Number of repetitions of increments in 10 steps.Defaults to20

steps

Numeric.Number of random steps to begin with.Defaults to10

iter

Numeric.Number of iterations for each random walk.Defaults to10000

cores

Numeric.Number of computer processing cores to use for bootstrapping samples.Defaults ton - 1 total number of cores.Set to any number between 1 and maximum amount of cores on your computer

Value

A result matrix containing the means and standard deviations forseveral measures as well asp-values for a Mann-Whitney U test

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com> and Yoed Kenett <yoedkenett@gmail.com>

References

Examples

# Simulate Datasetsone <- sim.fluency(10)two <- sim.fluency(10)# Compute similarity matrixcos1 <- similarity(one, method = "cosine")cos2 <- similarity(two, method = "cosine")# Compute networksnet1 <- TMFG(cos1)net2 <- TMFG(cos2)# Run random walk analysisrw.results <- randwalk(net1, net2, iter = 100, cores = 2)

Response Analysis

Description

Computes the difference in the total and uniquenumber of responses between two groups (follows Christensen et al., 2018)

Usage

response.analysis(...)

Arguments

...

Matrix or data frame.Responses matrices for two different groups

Value

A list containing objects:

total

A vector with the total responses given by each participant. At-testis used to compare, on average, whether one group provides more responsethan the other

unique

A vector with the number of unique responses provided by both groups(Total Across Groups), the number of unique responses provided byeach group (Total), and the number of unique resposnes provided by eachgroup that werenot provided by the other group (Unique). AMcNemar's test is used to compare whether the number of unique responsesare different between groups

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Examples

# Obtain datalow <- open.clean[which(open.group == "Low"),]high <- open.clean[which(open.group == "High"),]# Perform analysisresponse.analysis(low, high)

Semantic Network Measures

Description

Computes the average shortest path length (ASPL),clustering coefficient(CC), and modularity (Q) of the network

Usage

semnetmeas(A, meas = c("ASPL", "CC", "Q"), weighted = FALSE)

Arguments

A

Matrix or data frame.An adjacency matrix of a network

meas

Character.Global network measures to compute.By default, computes ASPL, CC, and Q.Individual measures can be selected

weighted

Boolean.Should weighted measures be computed?Defaults toFALSE.Set toTRUE for weighted measures

Value

Returns a values for ASPL, CC, and Q

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Datasetsone <- sim.fluency(10)# Compute similarity matrixcos <- similarity(one, method = "cosine")# Compute networksnet <- TMFG(cos)# Compute global network measuresglobmeas <- semnetmeas(net)

Simulates a verbal fluency binary response matrix

Description

Simulates verbal fluency data based on the number ofnodes in the desired network. The summed total ofeach response is simulated from a poisson distribution (seerpois), using frequencies from theanimals.freq data. Using thesesums, participants responses are simulated with a probability of givinga response as the total of the summed response over the number of participants.

Usage

sim.fluency(nodes, cases, random = FALSE)

Arguments

nodes

Numeric.Number of nodes to simulate in data.Defaults to100

cases

Numeric.Number of participants to simulate in data.Defaults to500

random

Boolean.Should the frequencies be randomly sampled from?Defaults toFALSE.

Value

A binary matrix withp (participants) byn (nodes)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate data for 50 nodes and 200 participantssim.fluency(nodes = 50, cases = 200)

Measures of Similarity

Description

Computes several measures of similarity(see Choi, Cha, & Tappert, 2010 for additional measures)

Usage

similarity(  data,  method = c("angular", "cor", "cosine", "euclid", "faith", "jaccard", "phi", "rr"))

Arguments

data

Matrix or data frame.A binarized dataset of verbal fluency or linguistic data

method

Character.Type of similarity measure to compute.

Below are the definitions for each bin:

	1	0
1	a	b	a+b	(R1)
0	c	d	c+d	(R2)
	a+c	b+d	a+b+c+d	(N)
	(C1)	(C2)	(N)

Options include:

"angular" =1 - (2 * acos(cosine similarity) / \pi)
"cosine" =a / \sqrt{(a + b)(a + c)}
"faith" =a + 0.5d / a + b + c + d
"jaccard" =a / a + b + c
"phi" and"cor" =ad - bc / \sqrt(R1 x R2 x C1 x C2)
"rr" =a / a + b + c + d

Value

A symmetric similarity matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Choi, S. S., Cha, S. H., & Tappert, C. C. (2010).A survey of binary similarity and distance measures.Journal of Systemics, Cybernetics and Informatics,8, 43-48.

Examples

# Simulate Datasetsone <- sim.fluency(10)# Compute similarity matrixcos <- similarity(one, method = "cosine")

Statistical tests for`bootSemNeT`

Description

Computes statistical tests for bootstrappednetworks frombootSemNeT

Usage

test.bootSemNeT(  ...,  test = c("ANCOVA", "ANOVA", "t-test"),  measures = c("ASPL", "CC", "Q"),  formula = NULL,  groups = NULL)

Arguments

...

Object(s) frombootSemNeT

test

Character.Type of statistical test to be used.

"t-test" — Computes t-tests for all measures
"ANOVA" — Computes ANOVAs and includes Tukey's HSD for pairwisecomparisons (TukeyHSD)
"ANCOVA" — Computes ANCOVAs that control for the number of nodesand edges in the networks and includes adjusted means and Tukey's HSD forpairwise comparisons (TukeyHSD)

measures

Character.Network measures to be tested.

ASPL — Average shortest path length
CC — Clustering coefficient
Q — Modularity statistic

formula

Character.A formula for specifying an ANOVA structure. The formula shouldhave the predictor variable as "y" and include the names the variablesare grouped by (e.g.,formula = "y ~ group_var1 * group_var2").See Two-way ANOVA example in examples

groups

Data frame.A data frame specifying the groups to be input into the formula.The column names should be the variable names of interest. Thegroups should be in the same order as the groups input intobootSemNeT

Value

Returns a list containing the objects:

ASPL

Test statistics for each proportion of nodes remaining for ASPL

CC

Test statistics for each proportion of nodes remaining for CC

Q

Test statistics for each proportion of nodes remaining for Q

If two groups:

A matrix in each object has the following columns:

t-statistic

Statistic from thet.test

df

Degrees of freedom

p-value

p-value with values equal to0 beingp < .001

d

Cohen'sd

CI95.lower

Lower bound of the 95 percent confidence interval

CI95.upper

Upper bound of the 95 percent confidence interval

Direction

Direction of the effect. The argumentgroups willspecify specifically which group is higher or lower on the measure. If nogroups are input, then"d" and"p" are used to representdata andpaired samples frombootSemNeT, respectively

Row names refer to the proportion of nodes remaining in bootstrapped networks

If three or more groups:

A list containing two objects:

ANOVA

A matrix containing theF-statistic, group degrees of freedom,residual degrees of freedom,p-value, and partial eta squaredp.eta.sq

HSD

A matrix containing the differences between each group (diff),lower (lwr) and upper (upr) bounds of the 95% confidence interval,and the adjustedp-value (p.adj)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Simulate Datasetone <- sim.fluency(20)two <- sim.fluency(20)# Run partial bootstrap networkstwo.result <- bootSemNeT(one, two, prop = .50, iter = 100,sim = "cosine", cores = 2, type = "node", method = "TMFG")# Compute teststest.bootSemNeT(two.result)# Two-way ANOVA example## Simulated datahihi <- sim.fluency(50, 500)hilo <- sim.fluency(50, 500)lohi <- sim.fluency(50, 500)lolo <- sim.fluency(50, 500)## Create groupsgroups <- matrix(c("high", "high","high", "low","low", "high","low", "low"), ncol = 2, byrow = TRUE)## Change column names (variable names)colnames(groups) <- c("gf","caq")## Run partial bootstrap networksboot.fifty <- bootSemNeT(hihi, hilo, lohi, lolo, prop = .50,type = "node", method = "TMFG", cores = 2, iter = 100)boot.sixty <- bootSemNeT(hihi, hilo, lohi, lolo, prop = .60,type = "node", method = "TMFG", cores = 2, iter = 100)## Compute teststest.bootSemNeT(boot.fifty, boot.sixty,test = "ANOVA", formula = "y ~ gf*caq", groups = groups)

Simulated Result for Dataset One and Two

Description

A result ofbootSemNeT from two simulated datasets

Usage

data(two.result)

Format

two.result (list, length = 6)

Examples

data("two.result")

Plots for Vignette

Description

Plots for vignette taken from Christensen & Kenett (2019)

Usage

data(vignette.plots)

Format

vignette.plots (list, length = 3)

References

Christensen, A. P., & Kenett, Y. N. (2019)Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks.PsyArXiv.

Examples

data("vignette.plots")

Movatterモバイル変換

SemNeT–package

Description

Author(s)

References

See Also

Average Shortest Path Length

Description

Usage

Arguments

Value

Author(s)

References

Examples

Clustering Coefficient

Description

Usage

Arguments

Value

Author(s)

References

Examples

Community Network Estimation

Description

Usage

Arguments

Value

Author(s)

References

Examples

Naive Random Walk Network Estimation

Description

Usage

Arguments

Value

Author(s)

References

Examples

Pathfinder Network

Description

Usage

Arguments

Value

Author(s)

References

Examples

Modularity

Description

Usage

Arguments

Value

Author(s)

References

Examples

Shiny App forSemNeT

Description

Usage

Value

Examples

Triangulated Maximally Filtered Graph

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Frequency of Animal Responses

Description

Usage

Format

Examples

Bootstrapped Semantic Network Analysis

Description

Usage

Arguments

Value

Author(s)

Examples

Shiny App for`SemNeT`